CN112820397B - Method for establishing peri-operative risk prediction model of coronary artery bypass grafting - Google Patents
Method for establishing peri-operative risk prediction model of coronary artery bypass grafting Download PDFInfo
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Abstract
The invention discloses a method for establishing a coronary artery bypass grafting perioperative risk prediction model, which combines collection and arrangement of a large number of domestic patient hospital data, a single factor and multi-factor analysis method and adopts a logisitc regression secondary prediction method to analyze to obtain a secondary prediction model, so that the variety of risk factors examined in the modeling process is enriched, the accuracy and reliability of model prediction are effectively improved, an effective assessment prediction method is provided for the coronary artery bypass grafting risk prediction of heart failure patients in China, the operation risk can be effectively reduced, and the method has positive significance for the health development of medical health industry in China.
Description
Technical Field
The invention belongs to the field of biomedicine, and particularly relates to a method for establishing a coronary artery bypass grafting perioperative risk prediction model.
Background
Coronary heart disease surgery risk prediction is a key link for identifying high-risk patients, reducing operation mortality and improving surgery curative effect. Especially in patients with heart failure, the surgical risk is obviously improved, and an accurate preoperative risk factor prediction model is more needed. At present, the main heart surgery risk scoring models at home and abroad comprise Euro SCORE, euro SCORE II, STS, sinoSCORE and the like for heart failure patient row CABG, and researches show that Euro SCORE, euro SCORE II and Sino SCORE for Chinese people can not accurately predict the hospital mortality of heart failure patient row CABG, and the mortality is obviously overestimated. The main disadvantages of the existing predictive model are: (1) The data for the creation of these models is relatively long-term (most of the 10 years old), and advances in surgical techniques and improvements in perioperative treatment levels have now significantly reduced post-CABG mortality. (2) Most models are built based on data of European populations, the population specificity is not strong, for example EuroSCORE II, the coverage population is mainly European populations, the incidence characteristics of coronary heart disease and heart failure are different from those of Chinese populations, and therefore the death rate of the Chinese heart failure line CABG operation is obviously overestimated. (3) Almost all existing models are directed to the population with common coronary heart disease, and not heart failure, such as SinoSCORE, as a predictive model established based on national data, which does not further distinguish between the population with EF <50% heart failure, so the prediction of mortality of this population is not accurate enough. Therefore, a prediction model suitable for the characteristics of the crowd in China is developed and used for predicting the peri-operative risk of the coronary artery bypass grafting of heart failure patients, and the prediction model is used as a reference and a basis for selecting a treatment method means and has important reference significance for improving the treatment success rate and reducing the operation risk.
Disclosure of Invention
The invention aims to provide a method for establishing a coronary artery bypass grafting perioperative risk prediction model, which can more accurately predict the coronary artery bypass grafting operation risk of heart failure patients, is more suitable for Chinese people, and is suitable for popularization in clinical practice.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for establishing a coronary artery bypass grafting perioperative risk prediction model is characterized in that the prediction model obtains relevant risk factors through regression analysis and selection of the coronary artery bypass grafting perioperative risk factors, obtains final risk factors through multi-factor regression analysis and screening of the relevant risk factors, obtains an initial prediction model through regression analysis, and finally optimizes the initial prediction model through secondary prediction to obtain the prediction model.
Further, the building of the model comprises the following steps:
(1) Collecting clinical data of patients with preoperative heart failure subjected to coronary artery bypass grafting operation and establishing a disease database;
(2) Respectively carrying out correlation analysis on the initial risk factors, selecting the correlated risk factors as research objects, wherein the selection basis is that the p value is less than 0.1 and the occurrence rate is more than 5 percent;
(3) Carrying out logistic regression analysis on the correlation risk factors in the step (2), carrying out multi-factor collinearity judgment, and removing the collinearity variables;
(4) Removing co-linear variablesThen, obtaining final risk factors, performing model fitting by using logistic regression, and obtaining regression equation partial regression coefficient beta i Constant beta in regression equation 0 Obtaining an initial prediction model;
(5) Setting a primary judgment value, performing primary prediction analysis, and dividing sample data into three groups: death group, security group, and uncertainty group;
(6) Setting a secondary judgment value, and carrying out logistic analysis on the uncertain group in the step (5) to obtain a secondary prediction model. The improvement of the initial prediction model can be realized, and the accuracy and reliability of prediction are improved.
Further, the initial risk factors of step (2) include: sex, hyperlipidemia, brain natriuretic peptide, history of thyroid dysfunction, hemoglobin, alanine aminotransferase, hypertension, body mass index, myocardial infarction, diabetes, stent implantation of cardiac vessels, elevated creatinine, cardiac surgery, history of smoking, peripheral arterial lesions, cerebrovascular events, pre-operative critical conditions, CCS grade 4, preoperative atrial fibrillation or flutter, NYHA class III or IV, left ventricular ejection fraction (35% < LVEF < 45%, LVEF < 35%), combined valve surgery, combined aortic surgery, non-optional surgery and extracorporeal circulation surgery.
Further, the related risk factors of step (2) include: sex, hyperlipidemia, brain natriuretic peptide, hemoglobin, elevated alanine Aminotransferase (ALT), body mass index, history of myocardial infarction, diabetes, elevated creatinine, history of prior cardiac surgery, cerebrovascular events, thyroid dysfunction, preoperative critical state, NYHA grade III or IV cardiac function, left ventricular ejection fraction (35% < LVEF < 45%, LVEF < 35%), concomitant valve surgery and concomitant aortic surgery.
Further, the final risk factor in step (4) includes: sex, elevated alanine Aminotransferase (ALT), brain natriuretic peptide, history of thyroid dysfunction, past heart surgery, elevated creatinine values, grade III or IV heart function, left ventricular ejection fraction, concomitant valve surgery, concomitant aortic surgery.
Further, the perioperative mortality prediction equation corresponding to the initial prediction model in the step (4) is:
further, xi in the prediction equation is a risk factor after screening, when the risk factor appears, xi=1, and when the risk factor does not appear, xi=0.
Further, the value of beta 0 in the prediction equation is-3.273.
Further, the primary judgment values in the step (5) are 0.7 and 0.3, when the predicted probability value reaches or exceeds 0.7, the judgment is made as a death group, when the predicted probability value is lower than 0.3, the judgment is made as a survival group, and the rest is an uncertain group.
Further, the secondary determination value in the step (6) is 0.6.
Compared with the existing other models, the model building process surveys and gathers the cardinal number of the diseased crowd, the database samples are more abundant, the inspected risk factors are more, the secondary prediction analysis is carried out on the uncertain crowd through the multi-step screening of the initial risk factors, the correlation risk factors and the final risk factors and the setting of the primary judgment value and the secondary judgment value, and the accuracy of model prediction is greatly improved through the multi-step screening and the secondary prediction method, so that the model is more suitable for Chinese crowd compared with the existing other prediction models, the prediction accuracy is higher, and the model has important risk guiding significance for clinical practice.
According to the invention, through expansion of a database of diseased people and a single-factor and multi-factor regression analysis method, the screened risk factors of brain natriuretic peptide, thyroid function and alanine aminotransferase are proved by experiments, and the accuracy of model prediction is improved effectively by the risk factors, so that the model prediction accuracy is stronger, and the method is more suitable for popularization in clinical practice.
According to the invention, by introducing new risk factors and combining a modeling method of secondary prediction, the prediction accuracy and reliability of the model are effectively improved, and the method has important guiding significance for clinical operation.
The risk factors brain natriuretic peptide, thyroid function and alanine aminotransferase provide more references and evaluation basis for predicting the peri-operative risk of the coronary artery bypass grafting of heart failure patients, and provide more basis and research methods for further deeply researching the reasons and mechanisms of the peri-operative risk of the coronary artery bypass grafting of heart failure patients.
Drawings
FIG. 1 is a schematic flow chart of a method for establishing a model for predicting the peri-operative risk of a coronary artery bypass grafting.
Detailed Description
The invention will be further described with reference to the following examples in conjunction with the flow diagram (fig. 1).
Example 1
The medical treatment and collection of 3659 domestic patients with complete clinical data subjected to coronary artery bypass grafting due to heart failure as modeling study objects in 2010 to 2019, and the medical treatment and collection of the integrity of the patient data and the actual collection result comprise the following steps: sex, hyperlipidemia, brain natriuretic peptide, history of thyroid dysfunction, hemoglobin, alanine aminotransferase, hypertension, body mass index, myocardial infarction, diabetes, stent implantation of cardiac vessels, elevated creatinine, cardiac surgery, smoking history, peripheral arterial lesions, cerebrovascular events, pre-operative critical state, CCS4 grade, preoperative atrial fibrillation or flutter, NYHA (heart failure) cardiac function III or IV grade, left ventricular ejection fraction (35% < LVEF < 45%, LVEF < 35%), combined valve surgery, combined aortic surgery, non-selective surgery and extracorporeal circulation surgery, 25 initial risk factors as study objects, hyperlipidemia judgment criteria, when the following fasting plasma examination index is not less than 1 item, can diagnose dyslipidemia, total Cholesterol (TC) not less than 6.2mmol/L, low density lipoprotein cholesterol (LDL-C) not less than 4.1mmol/L, triglyceride (TG) not less than 2.3mmol/L, high density lipoprotein cholesterol (HDL-C) not less than 1.0mmol/L; the brain natriuretic peptide classification criteria are: less than 50 years old, brain natriuretic peptide > 450pg/ml, between 50 and 75 years old, brain natriuretic peptide > 900pg/ml, greater than 75 years old, brain natriuretic peptide greater than 1800pg/ml; thyroid function, whether there is a history of thyroid abnormalities; hemoglobin, with < 90g/L as demarcation point; alanine aminotransferase, whether there is an increase in alanine aminotransferase; hypertension, whether there is a systolic pressure > 140mmHg or a diastolic pressure > 90mmHg; whether there is a history of myocardial infarction; whether diabetes has a history of sugar sickness; the stent implantation of the cardiac blood vessel is carried out in the prior art, and whether the stent implantation operation of the cardiac blood vessel is carried out or not; blood creatinine, preoperative blood creatinine > 176umol/L; heart surgery, whether there is heart surgery with open pericardium; a history of smoking, whether there is a history of smoking; peripheral arterial lesions, whether peripheral arterial lesions exist in the past; cerebrovascular events, with or without coma or central nervous system abnormalities for more than 24 hours, for more than 72 hours; a pre-operative critical state, whether any of ventricular tachycardia or ventricular fibrillation or sudden death from rescue; CCS grade 4, CCS angina classification grade 4; preoperative atrial fibrillation or atrial augmentation, and preoperative atrial fibrillation or atrial augmentation is not present in two weeks; the left ventricular ejection fraction is divided into two cases that LVEF is more than 35% and less than 45% and LVEF is less than or equal to 35%; combining valve operations, whether there are any valve operations combined; and merging aortic operations, and whether any merging aortic operations exist. The patient information is arranged, a patient information database is established, 3659 patients are divided into two groups according to age, sex and body quality index indexes, one group serves as a building module, the other group serves as a verification group, the number of the groups of the modeling group is 2365, the number of the persons in the verification group is 1294, the distribution conditions of the age, sex and body quality index indexes of the building module and the verification group are basically consistent, the actual mortality of the modeling group is 1.47%, and the actual mortality of the verification group is 1.42%.
Example 2
For the modeling group in example 1, single factor analysis is performed, the relationship between each single factor in 25 initial risk factors and perioperative mortality is analyzed, risk factors with p value less than 0.1 and occurrence rate more than 5% are screened, and the relevant risk factors obtained by the condition screening are as follows: sex, hyperlipidemia, brain natriuretic peptide, hemoglobin, alanine Aminotransferase (ALT) elevation, body mass index, myocardial infarction history, diabetes, elevated creatinine, past cardiac surgery, cerebrovascular events, history of thyroid dysfunction, preoperative critical state, NYHA class III or IV, left ventricular ejection fraction (35% < LVEF < 45%, LVEF < 35%), combined valve surgery and combined aortic surgery 17 risk factors were performed using SPSS 20.0.
Example 3
And (3) carrying out multi-factor collinearity judgment on 17 relevant risk factors in the step (2) by adopting logistic regression analysis, removing the collinearity variable, and removing 7 risk factors including hyperlipidemia, hemoglobin, body quality index, myocardial infarction medical history, diabetes, cerebrovascular events and preoperative critical states to obtain the complex composition comprising: gender, elevated alanine Aminotransferase (ALT), brain natriuretic peptide, history of thyroid dysfunction, past cardiac surgery, elevated creatinine levels, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery, combined aortic surgery, 10 final risk factors (11 independent factors), 10 final risk factors and weights are shown in table 1.
TABLE 1 Risk factor regression coefficient
The method comprises the following steps: gender, elevated alanine Aminotransferase (ALT), brain natriuretic peptide, history of thyroid dysfunction, past cardiac surgery, elevated creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, hemoglobin, combined valve surgery, combined aortic surgery, and performing logistic regression of 10 final risk factors to obtain partial regression coefficients βi and β0, and obtaining an initial prediction model, wherein the perioperative mortality prediction equation is:the parameters required in the equation are shown in table 1, xi is the risk factor (xi=1 if present), βi is the β coefficient of the corresponding variable in table 1, β0= -3.273, through which mortality prediction can be performed, 1000 people are selected from the validation group, divided according to age, sex and body mass indexAnd 5 groups, wherein the actual mortality of each group is consistent, 3 deaths are ensured in each 200 groups, the actual mortality of each group is 1.50%, and model prediction is carried out on the 5 groups, so that the prediction result is as follows: 1.50.+ -. 0.29% and the statistical analysis used was done with SPSS 20.0.
Example 4
In order to further improve the accuracy of model prediction, a secondary prediction model is built, on the basis of the embodiment 3, a primary judgment value is determined according to the regression prediction result of the embodiment 3, primary prediction analysis is performed, the model is built to select 0.7 and 0.3 as primary judgment values, when the prediction probability value reaches or exceeds 0.7, the model is judged to be a death group, when the prediction probability value is lower than 0.3, the model is judged to be a survival group, and the rest is an uncertain group, so that the obtained death rate prediction equation beta is obtained 1 = -5.915, mortality equation:then, carrying out secondary prediction analysis on the uncertain group, determining a new secondary judgment value according to the previous probability information and loss information by secondary prediction, judging the new judgment value of the model as a death group when the prediction probability value reaches or exceeds 0.6, judging the death group as a survival group when the prediction probability value is lower than 0.6, and obtaining a death rate prediction equation beta 2 = -4.173, quadratic predictive model mortality equation: />The secondary prediction analysis further improves the accuracy and reliability of prediction. Relevant regression analysis parameters in the predictive model are shown in Table 2.
TABLE 2 multifactor regression coefficients
Example 5
2000 people are selected from the modeling group, 5 groups are divided according to age, sex and body quality index indexes, the actual mortality of each group is guaranteed to be consistent, 6 deaths exist in each 400 people group, the actual mortality of each group is 1.50%, a secondary prediction model is adopted for model prediction, and the prediction result is that: mortality is (1.50+/-0.18)%, 1000 persons are selected from the verification group, 5 groups are divided according to age, sex and body quality index indexes, the actual mortality of each group is consistent, 3 deaths are ensured in each 200 person group, the actual mortality of each group is 1.50%, model prediction is carried out by adopting a secondary prediction model, and the prediction result is that: the mortality rate is (1.50+/-0.16)%, and the reliability and stability of the secondary prediction model are further verified by the grouping prediction results of the building module and the verification group.
The 5 groups of the above verification groups were predicted using EuroSCORE, euroSCORE II and SinoSCORE to obtain predicted mortality rates of (3.97.+ -. 0.45)%, (2.38.+ -. 0.52)% and (7.82.+ -. 0.36)%, respectively.
Through the comparison, the EuroSCORE, euroSCORE II and SinoSCORE predictive model established on the basis of European and American species data has larger deviation between the predicted result and the actual death rate of Chinese people.
Example 6
Verification experiment of the effect of three risk factors on secondary predictive model for alanine aminotransferase elevation, brain natriuretic peptide, and history of thyroid dysfunction: the verification object is 5 groupings of the verification group in example 5, with the purpose of verifying the accuracy of the secondary predictive model predictions when incorporating different risk factors. 1) 7 risk factors: sex, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery, plus elevated risk factor alanine aminotransferase, for a total of 8 risk factors, the predicted outcome is: (1.50±0.39)%; 2) 7 risk factors: gender, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery, plus brain natriuretic peptide, when total 8 risk factors, the predicted outcome is: (1.50±0.41)%; 3) 7 risk factors: sex, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery, plus risk factors for history of thyroid dysfunction, for a total of 8 risk factors, the predicted outcome is: (1.50±0.37)%; 4) 7 risk factors: sex, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery, plus two risk factors for elevated brain natriuretic peptide and alanine aminotransferase, total 9 risk factors, predicted results are: (1.50±0.33)%; 5) 7 risk factors: sex, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery, plus two risk factors of brain natriuretic peptide and history of thyroid dysfunction, when 9 risk factors are summed, the predicted outcome is: (1.50±0.37)%; 6) 7 risk factors: sex, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery, plus two risk factors, alanine aminotransferase elevation and a history of thyroid dysfunction, together with 9 risk factors, the predicted outcome is: (1.50±0.40)%; 7) Only 7 risk factors were included: the predicted results for gender, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery are: (1.50±0.48)%; 8) 7 risk factors: sex, past cardiac surgery, elevated blood creatinine values, grade III or IV cardiac function, left ventricular ejection fraction, combined valve surgery and combined aortic surgery, plus 3 risk factors for brain natriuretic peptide, elevated alanine aminotransferase and history of thyroid dysfunction, namely 10 final risk factors according to the invention, the model death prediction results are: (1.50.+ -. 0.16)%.
Through the analysis experiments of different combinations of the three risk factors, three risk factors can be obtained: the lack of either or both risk factors has a direct impact on the accuracy of the model's predictions, including elevated alanine aminotransferase, brain natriuretic peptide, and history of thyroid dysfunction.
When three risk factors including alanine aminotransferase elevation, brain natriuretic peptide and thyroid gland dysfunction history are added simultaneously in the 7 risk factors, the model prediction effect obtained by the establishment is best, the death prediction result is (1.50+/-0.16)%, the confidence interval is narrowest, the accuracy is highest, and the three risk factors are also further explained, the alanine aminotransferase elevation, brain natriuretic peptide and thyroid gland dysfunction history are indispensable for the establishment of a prediction model, and have important significance for the accuracy of model prediction established by the invention.
The foregoing examples are provided to facilitate understanding of the method and core ideas of the present application, and are not to be construed as limiting the present application where variations are apparent to those of ordinary skill in the art in light of the concepts of the present application.
Claims (4)
1. The method for establishing the peri-operative risk prediction model of the coronary artery bypass grafting is characterized in that the peri-operative risk prediction model obtains relevant risk factors through regression analysis and selection of the peri-operative risk factors of the coronary artery bypass grafting, obtains final risk factors through multi-factor regression analysis and screening of the relevant risk factors, obtains an initial prediction model through regression analysis, and finally optimizes the initial prediction model through secondary prediction to obtain the peri-operative risk prediction model;
the establishment of the perioperative risk prediction model comprises the following steps:
(1) Collecting clinical data of patients with preoperative heart failure subjected to coronary artery bypass grafting operation and establishing a disease database;
(2) Respectively carrying out correlation analysis on the initial risk factors, selecting the correlated risk factors as research objects, wherein the selection basis is that the p value is less than 0.1 and the occurrence rate is more than 5 percent;
(3) Carrying out logistic regression analysis on the related risk factors in the step (2), carrying out multi-factor collinearity judgment, and removing the collinearity variable;
(4) Removing the collinearity variable to obtain a final risk factor, and performing model fitting by using logistic regression to obtain a regression equation partial regression coefficient beta i Constant beta in regression equation 0 Obtaining an initial prediction model;
(5) Setting a primary judgment value, performing primary prediction analysis, and dividing sample data into three groups: death group, security group, and uncertainty group;
(6) Setting a secondary judgment value, and carrying out logistic analysis on the uncertain group in the step (5) to obtain the perioperative risk prediction model;
the initial risk factors of step (2) include: sex, hyperlipidemia, brain natriuretic peptide, history of thyroid dysfunction, hemoglobin, alanine aminotransferase, hypertension, body mass index, myocardial infarction, diabetes, stent implantation of cardiac vessels, elevated blood creatinine, cardiac surgery, history of smoking, peripheral arterial lesions, cerebrovascular events, preoperative critical conditions, CCS grade 4, preoperative atrial fibrillation or flutter, NYHA cardiac function grade III or IV, left ventricular ejection fraction, combined valve surgery, combined aortic surgery, non-selective surgery and extracorporeal circulation surgery;
the related risk factors of step (2) include: sex, hyperlipidemia, brain natriuretic peptide, hemoglobin, elevated alanine aminotransferase, body mass index, history of myocardial infarction, diabetes, elevated creatinine, past heart surgery, cerebrovascular events, history of thyroid dysfunction, preoperative critical state, NYHA heart function grade III or IV, left ventricular ejection fraction, combined valve surgery and combined aortic surgery;
the final risk factors in step (4) include: sex, elevated alanine aminotransferase, brain natriuretic peptide, history of thyroid dysfunction, past heart surgery, elevated creatinine values, NYHA heart function grade III or IV, left ventricular ejection fraction, concomitant valve surgery, concomitant aortic surgery;
and when three risk factors including alanine aminotransferase elevation, brain natriuretic peptide and thyroid dysfunction history are simultaneously added into the risk factors of the final risk factors, establishing a death prediction result of the perioperative risk prediction model to be (1.50+/-0.16)%.
2. The method for building a model for predicting peri-operative risk of coronary artery bypass grafting according to claim 1, wherein the initial model for predicting peri-operative mortality in step (4) has the following equation:
x in the perioperative mortality prediction equation i For the risk factors after screening, X is used when the risk factors appear i =1, risk factor is not present, X i =0; beta in the perioperative mortality prediction equation 0 Is-3.273, and beta in the perioperative mortality prediction equation i Is a partial regression coefficient.
3. The method for building a model for predicting the peri-operative risk of coronary artery bypass grafting according to claim 1, wherein the primary judgment values in the step (5) are 0.7 and 0.3, and when the predicted probability value is equal to or greater than 0.7, the predicted probability value is judged as a death group; when the predicted probability value is smaller than 0.3, judging the predicted probability value as a survival group; and when the predicted probability value is more than or equal to 0.3 and less than 0.7, judging the predicted probability value as an uncertain group.
4. The method for building a model for predicting peri-operative risk of coronary artery bypass grafting according to claim 1, wherein the secondary determination value in the step (6) is 0.6.
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